Abstract
This paper proposes a new formulation of Gaussian process for constraints with piece-wise smooth conditions. Combining ideas from decision trees and Gaussian processes, it is shown that the new model can effectively identify the non-smooth regions and tackle the non-smoothness in piece-wise smooth constraint functions. A constrained Bayesian optimizer is then constructed to handle optimization problems with both noisy objective and constraint functions.
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Gorji Daronkolaei, A., Hajian, A., Custis, T. (2018). Constrained Bayesian Optimization for Problems with Piece-wise Smooth Constraints. In: Bagheri, E., Cheung, J. (eds) Advances in Artificial Intelligence. Canadian AI 2018. Lecture Notes in Computer Science(), vol 10832. Springer, Cham. https://doi.org/10.1007/978-3-319-89656-4_18
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DOI: https://doi.org/10.1007/978-3-319-89656-4_18
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